Nightly Per-Antenna Quality Summary Notebook

Josh Dillon, Last Revised February 2021

This notebooks brings together as much information as possible from ant_metrics, auto_metrics and redcal to help figure out which antennas are working properly and summarizes it in a single giant table. It is meant to be lightweight and re-run as often as necessary over the night, so it can be run when any of those is done and then be updated when another one completes.

Contents:

• Table 1: Overall Array Health
• Table 2: RTP Per-Antenna Metrics Summary Table
• Figure 1: Array Plot of Flags and A Priori Statuses

import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
pd.set_option('display.max_rows', 1000)
from hera_qm.metrics_io import load_metric_file
from hera_cal import utils, io, redcal
import glob
import h5py
from copy import deepcopy
from IPython.display import display, HTML
from hera_notebook_templates.utils import status_colors
from hera_mc import mc
from pyuvdata import UVData

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
display(HTML("<style>.container { width:100% !important; }</style>"))

# If you want to run this notebook locally, copy the output of the next cell into the first few lines of this cell.

# JD = "2459122"
# data_path = '/lustre/aoc/projects/hera/H4C/2459122'
# ant_metrics_ext = ".ant_metrics.hdf5"
# redcal_ext = ".maybe_good.omni.calfits"
# nb_outdir = '/lustre/aoc/projects/hera/H4C/h4c_software/H4C_Notebooks/_rtp_summary_'
# good_statuses = "digital_ok,calibration_maintenance,calibration_triage,calibration_ok"
# os.environ["JULIANDATE"] = JD
# os.environ["DATA_PATH"] = data_path
# os.environ["ANT_METRICS_EXT"] = ant_metrics_ext
# os.environ["REDCAL_EXT"] = redcal_ext
# os.environ["NB_OUTDIR"] = nb_outdir
# os.environ["GOOD_STATUSES"] = good_statuses

# Use environment variables to figure out path to data
JD = os.environ['JULIANDATE']
data_path = os.environ['DATA_PATH']
ant_metrics_ext = os.environ['ANT_METRICS_EXT']
redcal_ext = os.environ['REDCAL_EXT']
nb_outdir = os.environ['NB_OUTDIR']
good_statuses = os.environ['GOOD_STATUSES']
print(f'JD = "{JD}"')
print(f'data_path = "{data_path}"')
print(f'ant_metrics_ext = "{ant_metrics_ext}"')
print(f'redcal_ext = "{redcal_ext}"')
print(f'nb_outdir = "{nb_outdir}"')
print(f'good_statuses = "{good_statuses}"')

JD = "2459832"
data_path = "/mnt/sn1/2459832"
ant_metrics_ext = ".ant_metrics.hdf5"
redcal_ext = ".known_good.omni.calfits"
nb_outdir = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
good_statuses = "digital_ok,calibration_maintenance,calibration_triage,calibration_ok"

from astropy.time import Time
utc = Time(JD, format='jd').datetime
print(f'Date: {utc.month}-{utc.day}-{utc.year}')

Date: 9-9-2022

# Per-season options
def ant_to_report_url(ant):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/antenna_report/antenna_{ant}_report.html'

Load Auto Metrics

use_auto_metrics = False

# find the auto_metrics file
glob_str = os.path.join(data_path, f'zen.{JD}*.auto_metrics.h5')
auto_metrics_file = sorted(glob.glob(glob_str))

# if it exists, load and extract relevant information
if len(auto_metrics_file) > 0:
    auto_metrics_file = auto_metrics_file[0]
    print(f'Found auto_metrics results file at {auto_metrics_file}.')
    
    auto_metrics = load_metric_file(auto_metrics_file)
    mean_round_modz_cut = auto_metrics['parameters']['mean_round_modz_cut']
    auto_ex_ants = auto_metrics['ex_ants']['r2_ex_ants']
    
    use_auto_metrics = True
else:
    print(f'No files found matching glob {glob_str}. Skipping auto_metrics.')

Found auto_metrics results file at /mnt/sn1/2459832/zen.2459832.25316.sum.auto_metrics.h5.

Load Ant Metrics

use_ant_metrics = False

# get a list of all ant_metrics files
glob_str = os.path.join(data_path, f'zen.{JD}.?????.sum{ant_metrics_ext}')
ant_metrics_files = sorted(glob.glob(glob_str))

# if they exist, load as many of them as possible
if len(ant_metrics_files) > 0:
    print(f'Found {len(ant_metrics_files)} ant_metrics files matching glob {glob_str}')
    ant_metrics_apriori_exants = {}
    ant_metrics_xants_dict = {}
    ant_metrics_dead_ants_dict = {}
    ant_metrics_crossed_ants_dict = {}
    ant_metrics_dead_metrics = {}
    ant_metrics_crossed_metrics = {}
    dead_cuts = {}
    crossed_cuts = {}
    for amf in ant_metrics_files:
        with h5py.File(amf, "r") as infile: # use h5py directly since it's much faster than load_metric_file
            # get out results for this file
            dead_cuts[amf] = infile['Metrics']['dead_ant_cut'][()]
            crossed_cuts[amf] = infile['Metrics']['cross_pol_cut'][()]
            xants = infile['Metrics']['xants'][:]
            dead_ants = infile['Metrics']['dead_ants'][:]
            crossed_ants = infile['Metrics']['crossed_ants'][:]        
            try:
                # look for ex_ants in history
                ex_ants_string = infile['Header']['history'][()].decode()
                ex_ants_string = ex_ants_string.split('--apriori_xants')[1]
                ex_ants_string = ex_ants_string.split('--')[0].strip()
            except:
                ex_ants_string = ''
                    
            # This only works for the new correlation-matrix-based ant_metrics
            if 'corr' in infile['Metrics']['final_metrics'] and 'corrXPol' in infile['Metrics']['final_metrics']:
                ant_metrics_dead_metrics[amf] = {eval(ant): infile['Metrics']['final_metrics']['corr'][ant][()]
                                                 for ant in infile['Metrics']['final_metrics']['corr']}
                ant_metrics_crossed_metrics[amf] = {eval(ant): infile['Metrics']['final_metrics']['corrXPol'][ant][()]
                                                    for ant in infile['Metrics']['final_metrics']['corrXPol']}                       
            else:
                raise(KeywordError)
        
        # organize results by file
        ant_metrics_xants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in xants]
        ant_metrics_dead_ants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in dead_ants]
        ant_metrics_crossed_ants_dict[amf] = [(int(ant[0]), ant[1].decode()) for ant in crossed_ants]
        ant_metrics_apriori_exants[amf] = [int(ant) for ant in ex_ants_string.split()]
    
    dead_cut = np.median(list(dead_cuts.values()))
    crossed_cut = np.median(list(crossed_cuts.values()))
        
    use_ant_metrics = True
else:
    print(f'No files found matching glob {glob_str}. Skipping ant_metrics.')

Found 372 ant_metrics files matching glob /mnt/sn1/2459832/zen.2459832.?????.sum.ant_metrics.hdf5

Load chi^2 info from redcal

use_redcal = False
glob_str = os.path.join(data_path, f'zen.{JD}.?????.sum{redcal_ext}')

redcal_files = sorted(glob.glob(glob_str))
if len(redcal_files) > 0:
    print(f'Found {len(redcal_files)} ant_metrics files matching glob {glob_str}')
    post_redcal_ant_flags_dict = {}
    flagged_by_redcal_dict = {}
    cspa_med_dict = {}
    for cal in redcal_files:
        hc = io.HERACal(cal)
        _, flags, cspa, chisq = hc.read()
        cspa_med_dict[cal] = {ant: np.nanmedian(cspa[ant], axis=1) for ant in cspa}

        post_redcal_ant_flags_dict[cal] = {ant: np.all(flags[ant]) for ant in flags}
        # check history to distinguish antennas flagged going into redcal from ones flagged during redcal
        tossed_antenna_lines =  hc.history.replace('\n','').split('Throwing out antenna ')[1:]
        flagged_by_redcal_dict[cal] = sorted([int(line.split(' ')[0]) for line in tossed_antenna_lines])
        
    use_redcal = True
else:
    print(f'No files found matching glob {glob_str}. Skipping redcal chisq.')

No files found matching glob /mnt/sn1/2459832/zen.2459832.?????.sum.known_good.omni.calfits. Skipping redcal chisq.

Figure out some general properties

# Parse some general array properties, taking into account the fact that we might be missing some of the metrics
ants = []
pols = []
antpol_pairs = []

if use_auto_metrics:
    ants = sorted(set(bl[0] for bl in auto_metrics['modzs']['r2_shape_modzs']))
    pols = sorted(set(bl[2] for bl in auto_metrics['modzs']['r2_shape_modzs']))
if use_ant_metrics:
    antpol_pairs = sorted(set([antpol for dms in ant_metrics_dead_metrics.values() for antpol in dms.keys()]))
    antpols = sorted(set(antpol[1] for antpol in antpol_pairs))
    ants = sorted(set(antpol[0] for antpol in antpol_pairs) | set(ants))
    pols = sorted(set(utils.join_pol(ap, ap) for ap in antpols) | set(pols))
if use_redcal:
    antpol_pairs = sorted(set([ant for cspa in cspa_med_dict.values() for ant in cspa.keys()]) | set(antpol_pairs))
    antpols = sorted(set(antpol[1] for antpol in antpol_pairs))
    ants = sorted(set(antpol[0] for antpol in antpol_pairs) | set(ants))
    pols = sorted(set(utils.join_pol(ap, ap) for ap in antpols) | set(pols))

# Figure out remaining antennas not in data and also LST range
data_files = sorted(glob.glob(os.path.join(data_path, 'zen.*.sum.uvh5')))
hd = io.HERAData(data_files[0])
unused_ants = [ant for ant in hd.antpos if ant not in ants]    
hd_last = io.HERAData(data_files[-1])

Load a priori antenna statuses and node numbers

# try to load a priori antenna statusesm but fail gracefully if this doesn't work.
a_priori_statuses = {ant: 'Not Found' for ant in ants}
nodes = {ant: np.nan for ant in ants + unused_ants}
try:
    from hera_mc import cm_hookup

    # get node numbers
    hookup = cm_hookup.get_hookup('default')
    for ant_name in hookup:
        ant = int("".join(filter(str.isdigit, ant_name)))
        if ant in nodes:
            if hookup[ant_name].get_part_from_type('node')['E<ground'] is not None:
                nodes[ant] = int(hookup[ant_name].get_part_from_type('node')['E<ground'][1:])
    
    # get apriori antenna status
    for ant_name, data in hookup.items():
        ant = int("".join(filter(str.isdigit, ant_name)))
        if ant in a_priori_statuses:
            a_priori_statuses[ant] = data.apriori

except Exception as err:
    print(f'Could not load node numbers and a priori antenna statuses.\nEncountered {type(err)} with message: {err}')

Summarize auto metrics

if use_auto_metrics:
    # Parse modzs
    modzs_to_check = {'Shape': 'r2_shape_modzs', 'Power': 'r2_power_modzs', 
                      'Temporal Variability': 'r2_temp_var_modzs', 'Temporal Discontinuties': 'r2_temp_diff_modzs'}
    worst_metrics = []
    worst_zs = []
    all_modzs = {}
    binary_flags = {rationale: [] for rationale in modzs_to_check}

    for ant in ants:
        # parse modzs and figure out flag counts
        modzs = {f'{pol} {rationale}': auto_metrics['modzs'][dict_name][(ant, ant, pol)] 
                 for rationale, dict_name in modzs_to_check.items() for pol in pols}
        for pol in pols:
            for rationale, dict_name in modzs_to_check.items():
                binary_flags[rationale].append(auto_metrics['modzs'][dict_name][(ant, ant, pol)] > mean_round_modz_cut)

        # parse out all metrics for dataframe
        for k in modzs:
            col_label = k + ' Modified Z-Score'
            if col_label in all_modzs:
                all_modzs[col_label].append(modzs[k])
            else:
                all_modzs[col_label] = [modzs[k]]
                
    mean_round_modz_cut = auto_metrics['parameters']['mean_round_modz_cut']
else:
    mean_round_modz_cut = 0

Summarize ant metrics

if use_ant_metrics:
    a_priori_flag_frac = {ant: np.mean([ant in apxa for apxa in ant_metrics_apriori_exants.values()]) for ant in ants}
    dead_ant_frac = {ap: {ant: np.mean([(ant, ap) in das for das in ant_metrics_dead_ants_dict.values()])
                                 for ant in ants} for ap in antpols}
    crossed_ant_frac = {ant: np.mean([np.any([(ant, ap) in cas for ap in antpols])
                                      for cas in ant_metrics_crossed_ants_dict.values()]) for ant in ants}
    ant_metrics_xants_frac_by_antpol = {antpol: np.mean([antpol in amx for amx in ant_metrics_xants_dict.values()]) for antpol in antpol_pairs}
    ant_metrics_xants_frac_by_ant = {ant: np.mean([np.any([(ant, ap) in amx for ap in antpols])
                                     for amx in ant_metrics_xants_dict.values()]) for ant in ants}
    average_dead_metrics = {ap: {ant: np.nanmean([dm.get((ant, ap), np.nan) for dm in ant_metrics_dead_metrics.values()]) 
                                 for ant in ants} for ap in antpols}
    average_crossed_metrics = {ant: np.nanmean([cm.get((ant, ap), np.nan) for ap in antpols 
                                                for cm in ant_metrics_crossed_metrics.values()]) for ant in ants}
else:
    dead_cut = 0.4
    crossed_cut = 0.0

Summarize redcal chi^2 metrics

if use_redcal:
    cspa = {ant: np.nanmedian(np.hstack([cspa_med_dict[cal][ant] for cal in redcal_files])) for ant in antpol_pairs}
    redcal_prior_flag_frac = {ant: np.mean([np.any([afd[ant, ap] and not ant in flagged_by_redcal_dict[cal] for ap in antpols])
                                            for cal, afd in post_redcal_ant_flags_dict.items()]) for ant in ants}
    redcal_flagged_frac = {ant: np.mean([ant in fbr for fbr in flagged_by_redcal_dict.values()]) for ant in ants}

Get FEM switch states

HHautos = sorted(glob.glob(f"{data_path}/zen.{JD}.*.sum.autos.uvh5"))
diffautos = sorted(glob.glob(f"{data_path}/zen.{JD}.*.diff.autos.uvh5"))

try:
    db = mc.connect_to_mc_db(None)
    session = db.sessionmaker()
    startJD = float(HHautos[0].split('zen.')[1].split('.sum')[0])
    stopJD = float(HHautos[-1].split('zen.')[1].split('.sum')[0])
    startTime = Time(startJD,format='jd')
    stopTime = Time(stopJD,format='jd')
    res = session.get_antenna_status(starttime=startTime, stoptime=stopTime)
    fem_switches = {}
    if len(res) == 0:
        femState = None
    else:
        for antpol in res:
            fem_switches[(antpol.antenna_number, antpol.antenna_feed_pol)] = antpol.fem_switch
    femState = (max(set(list(fem_switches.values())), key = list(fem_switches.values()).count)) 
except Exception as e:
    print(e)
    femState = None

max() arg is an empty sequence

Find X-engine Failures

read_inds = [1, len(HHautos)//2, -2]
x_status = [1,1,1,1,1,1,1,1]
s = UVData()
s.read(HHautos[1])

nants = len(s.get_ants())
freqs = s.freq_array[0]*1e-6
nfreqs = len(freqs)

antCon = {a: None for a in ants}
rightAnts = []
for i in read_inds:
    s = UVData()
    d = UVData()
    s.read(HHautos[i])
    d.read(diffautos[i])
    for pol in [0,1]:
        sm = np.abs(s.data_array[:,0,:,pol])
        df = np.abs(d.data_array[:,0,:,pol])
        sm = np.r_[sm, np.nan + np.zeros((-len(sm) % nants,len(freqs)))]
        sm = np.nanmean(sm.reshape(-1,nants,nfreqs),axis=1)
        df = np.r_[df, np.nan + np.zeros((-len(df) % nants,len(freqs)))]
        df = np.nanmean(df.reshape(-1,nants,nfreqs),axis=1)

        evens = (sm + df)/2
        odds = (sm - df)/2
        rat = np.divide(evens,odds)
        rat = np.nan_to_num(rat)
        for xbox in range(0,8):
            xavg = np.nanmean(rat[:,xbox*192:(xbox+1)*192],axis=1)
            if np.nanmax(xavg)>1.5 or np.nanmin(xavg)<0.5:
                x_status[xbox] = 0
    for ant in ants:
        for pol in ["xx", "yy"]:
            if antCon[ant] is False:
                continue
            spectrum = s.get_data(ant, ant, pol)
            stdev = np.std(spectrum)
            med = np.median(np.abs(spectrum))
            if (femState == "load" or femState == 'noise') and 80000 < stdev <= 4000000 and antCon[ant] is not False:
                antCon[ant] = True
            elif femState == "antenna" and stdev > 500000 and med > 950000 and antCon[ant] is not False:
                antCon[ant] = True
            else:
                antCon[ant] = False
            if np.min(np.abs(spectrum)) < 100000:
                antCon[ant] = False
for ant in ants:
    if antCon[ant] is True:
        rightAnts.append(ant)
            
x_status_str = ''
for i,x in enumerate(x_status):
    if x==0:
        x_status_str += '\u274C '
    else:
        x_status_str += '\u2705 '

Build Overall Health DataFrame

def comma_sep_paragraph(vals, chars_per_line=40):
    outstrs = []
    for val in vals:
        if (len(outstrs) == 0) or (len(outstrs[-1]) > chars_per_line):
            outstrs.append(str(val))
        else:
            outstrs[-1] += ', ' + str(val)
    return ',<br>'.join(outstrs)

# Time data
to_show = {'JD': [JD]}
to_show['Date'] = f'{utc.month}-{utc.day}-{utc.year}'
to_show['LST Range'] = f'{hd.lsts[0] * 12 / np.pi:.3f} -- {hd_last.lsts[-1] * 12 / np.pi:.3f} hours'

# X-engine status
to_show['X-Engine Status'] = x_status_str

# Files
to_show['Number of Files'] = len(data_files)

# Antenna Calculations
to_show['Total Number of Antennas'] = len(ants)

to_show[' '] = ''
to_show['OPERATIONAL STATUS SUMMARY'] = ''

status_count = {status: 0 for status in status_colors}
for ant, status in a_priori_statuses.items():
    if status in status_count:
        status_count[status] = status_count[status] + 1
    else:
        status_count[status] = 1
to_show['Antenna A Priori Status Count'] = '<br>'.join([f'{status}: {status_count[status]}' for status in status_colors if status in status_count and status_count[status] > 0])

to_show['Commanded Signal Source'] = femState
to_show['Antennas in Commanded State'] = f'{len(rightAnts)} / {len(ants)} ({len(rightAnts) / len(ants):.1%})'

if use_ant_metrics:
    to_show['Cross-Polarized Antennas'] = ', '.join([str(ant) for ant in ants if (np.max([dead_ant_frac[ap][ant] for ap in antpols]) + crossed_ant_frac[ant] == 1) 
                                                                                 and (crossed_ant_frac[ant] > .5)])

# Node calculations
nodes_used = set([nodes[ant] for ant in ants if np.isfinite(nodes[ant])])
to_show['Total Number of Nodes'] = len(nodes_used)
if use_ant_metrics:
    node_off = {node: True for node in nodes_used}
    not_correlating = {node: True for node in nodes_used}
    for ant in ants:
        for ap in antpols:
            if np.isfinite(nodes[ant]):
                if np.isfinite(average_dead_metrics[ap][ant]):
                    node_off[nodes[ant]] = False
                if dead_ant_frac[ap][ant] < 1:
                    not_correlating[nodes[ant]] = False
    to_show['Nodes Registering 0s'] = ', '.join([f'N{n:02}' for n in sorted([node for node in node_off if node_off[node]])])
    to_show['Nodes Not Correlating'] = ', '.join([f'N{n:02}' for n in sorted([node for node in not_correlating if not_correlating[node] and not node_off[node]])])

# Pipeline calculations    
to_show['  '] = ''
to_show['NIGHTLY ANALYSIS SUMMARY'] = ''
    
all_flagged_ants = []
if use_ant_metrics:
    to_show['Ant Metrics Done?'] = '\u2705'
    ant_metrics_flagged_ants = [ant for ant in ants if ant_metrics_xants_frac_by_ant[ant] > 0]
    all_flagged_ants.extend(ant_metrics_flagged_ants)
    to_show['Ant Metrics Flagged Antennas'] = f'{len(ant_metrics_flagged_ants)} / {len(ants)} ({len(ant_metrics_flagged_ants) / len(ants):.1%})' 
else:
    to_show['Ant Metrics Done?'] = '\u274C'
if use_auto_metrics:
    to_show['Auto Metrics Done?'] = '\u2705'
    auto_metrics_flagged_ants = [ant for ant in ants if ant in auto_ex_ants]
    all_flagged_ants.extend(auto_metrics_flagged_ants)    
    to_show['Auto Metrics Flagged Antennas'] = f'{len(auto_metrics_flagged_ants)} / {len(ants)} ({len(auto_metrics_flagged_ants) / len(ants):.1%})' 
else:
    to_show['Auto Metrics Done?'] = '\u274C'
if use_redcal:
    to_show['Redcal Done?'] = '\u2705'    
    redcal_flagged_ants = [ant for ant in ants if redcal_flagged_frac[ant] > 0]
    all_flagged_ants.extend(redcal_flagged_ants)    
    to_show['Redcal Flagged Antennas'] = f'{len(redcal_flagged_ants)} / {len(ants)} ({len(redcal_flagged_ants) / len(ants):.1%})' 
else:
    to_show['Redcal Done?'] = '\u274C' 
to_show['Never Flagged Antennas'] = f'{len(ants) - len(set(all_flagged_ants))} / {len(ants)} ({(len(ants) - len(set(all_flagged_ants))) / len(ants):.1%})'

# Count bad antennas with good statuses and vice versa
n_apriori_good = len([ant for ant in ants if a_priori_statuses[ant] in good_statuses.split(',')])
apriori_good_flagged = []
aprior_bad_unflagged = []
for ant in ants:
    if ant in set(all_flagged_ants) and a_priori_statuses[ant] in good_statuses.split(','):
        apriori_good_flagged.append(ant)
    elif ant not in set(all_flagged_ants) and a_priori_statuses[ant] not in good_statuses.split(','):
        aprior_bad_unflagged.append(ant)
to_show['A Priori Good Antennas Flagged'] = f'{len(apriori_good_flagged)} / {n_apriori_good} total a priori good antennas:<br>' + \
                                            comma_sep_paragraph(apriori_good_flagged)
to_show['A Priori Bad Antennas Not Flagged'] = f'{len(aprior_bad_unflagged)} / {len(ants) - n_apriori_good} total a priori bad antennas:<br>' + \
                                            comma_sep_paragraph(aprior_bad_unflagged)

# Apply Styling
df = pd.DataFrame(to_show)
divider_cols = [df.columns.get_loc(col) for col in ['NIGHTLY ANALYSIS SUMMARY', 'OPERATIONAL STATUS SUMMARY']]
try:
    to_red_columns = [df.columns.get_loc(col) for col in ['Cross-Polarized Antennas', 'Nodes Registering 0s', 
                                                          'Nodes Not Correlating', 'A Priori Good Antennas Flagged']]
except:
    to_red_columns = []
def red_specific_cells(x):
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    for col in to_red_columns:
        df1.iloc[col] = 'color: red'
    return df1

df = df.T
table = df.style.hide_columns().apply(red_specific_cells, axis=None)
for col in divider_cols:
    table = table.set_table_styles([{"selector":f"tr:nth-child({col+1})", "props": [("background-color", "black"), ("color", "white")]}], overwrite=False)

Table 1: Overall Array Health

HTML(table.render())

JD	2459832
Date	9-9-2022
LST Range	18.754 -- 20.754 hours
X-Engine Status	✅ ✅ ✅ ✅ ✅ ✅ ✅ ✅
Number of Files	372
Total Number of Antennas	139
OPERATIONAL STATUS SUMMARY
Antenna A Priori Status Count	dish_maintenance: 3 RF_maintenance: 32 RF_ok: 3 digital_maintenance: 3 digital_ok: 95 not_connected: 3
Commanded Signal Source	None
Antennas in Commanded State	0 / 139 (0.0%)
Cross-Polarized Antennas
Total Number of Nodes	14
Nodes Registering 0s	N18
Nodes Not Correlating	N04
NIGHTLY ANALYSIS SUMMARY
Ant Metrics Done?	✅
Ant Metrics Flagged Antennas	57 / 139 (41.0%)
Auto Metrics Done?	✅
Auto Metrics Flagged Antennas	89 / 139 (64.0%)
Redcal Done?	❌
Never Flagged Antennas	39 / 139 (28.1%)
A Priori Good Antennas Flagged	60 / 95 total a priori good antennas: 3, 7, 9, 10, 19, 20, 21, 29, 30, 31, 37, 38, 40, 41, 42, 45, 53, 54, 55, 56, 69, 71, 72, 73, 84, 86, 88, 91, 93, 94, 99, 101, 103, 105, 106, 107, 108, 118, 121, 122, 123, 128, 140, 141, 142, 144, 156, 158, 160, 161, 165, 167, 169, 170, 176, 177, 179, 181, 190, 191
A Priori Bad Antennas Not Flagged	4 / 44 total a priori bad antennas: 82, 90, 135, 138

# write to csv
outpath = os.path.join(nb_outdir, f'array_health_table_{JD}.csv')
print(f'Now saving Table 2 to a csv at {outpath}')
df.replace({'\u2705': 'Y'}, regex=True).replace({'\u274C': 'N'}, regex=True).replace({'<br>': ' '}, regex=True).to_csv(outpath)

Now saving Table 2 to a csv at /home/obs/src/H6C_Notebooks/_rtp_summary_/array_health_table_2459832.csv

Build DataFrame

# build dataframe
to_show = {'Ant': [f'<a href="{ant_to_report_url(ant)}" target="_blank">{ant}</a>' for ant in ants],
           'Node': [f'N{nodes[ant]:02}' for ant in ants], 
           'A Priori Status': [a_priori_statuses[ant] for ant in ants]}
           #'Worst Metric': worst_metrics, 'Worst Modified Z-Score': worst_zs}
df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
if use_auto_metrics:
    bar_cols['Auto Metrics Flags'] = [float(ant in auto_ex_ants) for ant in ants]
if use_ant_metrics:
    if np.sum(list(a_priori_flag_frac.values())) > 0:  # only include this col if there are any a priori flags
        bar_cols['A Priori Flag Fraction in Ant Metrics'] = [a_priori_flag_frac[ant] for ant in ants]
    for ap in antpols:
        bar_cols[f'Dead Fraction in Ant Metrics ({ap})'] = [dead_ant_frac[ap][ant] for ant in ants]
    bar_cols['Crossed Fraction in Ant Metrics'] = [crossed_ant_frac[ant] for ant in ants]
if use_redcal:
    bar_cols['Flag Fraction Before Redcal'] = [redcal_prior_flag_frac[ant] for ant in ants]
    bar_cols['Flagged By Redcal chi^2 Fraction'] = [redcal_flagged_frac[ant] for ant in ants]  
for col in bar_cols:
    df[col] = bar_cols[col]

# add auto_metrics
if use_auto_metrics:
    for label, modz in all_modzs.items():
        df[label] = modz
z_score_cols = [col for col in df.columns if 'Modified Z-Score' in col]        
        
# add ant_metrics
ant_metrics_cols = {}
if use_ant_metrics:
    for ap in antpols:
        ant_metrics_cols[f'Average Dead Ant Metric ({ap})'] = [average_dead_metrics[ap][ant] for ant in ants]
    ant_metrics_cols['Average Crossed Ant Metric'] = [average_crossed_metrics[ant] for ant in ants]
    for col in ant_metrics_cols:
        df[col] = ant_metrics_cols[col]   

# add redcal chisq
redcal_cols = []
if use_redcal:
    for ap in antpols:
        col_title = f'Median chi^2 Per Antenna ({ap})'
        df[col_title] = [cspa[ant, ap] for ant in ants]
        redcal_cols.append(col_title)

# sort by node number and then by antenna number within nodes
df.sort_values(['Node', 'Ant'], ascending=True)

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=z_score_cols) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=redcal_cols) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=z_score_cols) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=z_score_cols) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format({col: '{:,.2%}'.format for col in bar_cols}) \
          .applymap(lambda val: 'font-weight: bold', subset=['Ant']) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 2: RTP Per-Antenna Metrics Summary Table

This admittedly very busy table incorporates summary information about all antennas in the array. Its columns depend on what information is available when the notebook is run (i.e. whether auto_metrics, ant_metrics, and/or redcal is done). These can be divided into 5 sections:

Basic Antenna Info: antenna number, node, and its a priori status.

Flag Fractions: Fraction of the night that an antenna was flagged for various reasons. Note that auto_metrics flags antennas for the whole night, so it'll be 0% or 100%.

auto_metrics Details: If auto_metrics is included, this section shows the modified Z-score signifying how much of an outlier each antenna and polarization is in each of four categories: bandpass shape, overall power, temporal variability, and temporal discontinuities. Bold red text indicates that this is a reason for flagging the antenna. It is reproduced from the auto_metrics_inspect.ipynb nightly notebook, so check that out for more details on the precise metrics.

ant_metrics Details: If ant_metrics is included, this section shows the average correlation-based metrics for antennas over the whole night. Low "dead ant" metrics (nominally below 0.4) indicate antennas not correlating with the rest of the array. Negative "crossed ant" metrics indicate antennas that show stronger correlations in their cross-pols than their same-pols, indicating that the two polarizations are probably swapped. Bold text indicates that the average is below the threshold for flagging.

redcal chi^2 Details: If redcal is included, this shows the median chi^2 per antenna. This would be 1 in an ideal array. Antennas are thrown out when they they are outliers in their median chi^2, usually greater than 4-sigma outliers in modified Z-score.

HTML(table.render())

Ant	Node	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric
3	N01	digital_ok	100.00%	0.00%	0.00%	0.00%	4.805642	-0.890584	-0.819767	-0.865884	-0.296672	-0.038974	-0.485155	1.196451	0.792909	0.499739	0.606156
4	N01	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-0.713393	7.652966	0.352123	0.738537	-0.543284	1.357620	0.563172	-0.738605	0.806828	0.499168	0.614114
5	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	0.004625	-0.134983	0.801247	2.395085	-1.084607	1.391563	-0.203264	-1.272611	0.809931	0.517612	0.610504
7	N02	digital_ok	100.00%	0.00%	16.13%	0.00%	-0.942873	-1.117721	-0.201153	-0.651515	-0.456023	-0.899052	0.795148	8.134188	0.747778	0.435631	0.576052
8	N02	RF_maintenance	100.00%	0.00%	29.57%	0.00%	10.997295	12.606926	16.046036	16.257564	8.473974	8.221652	1.589203	-0.851799	0.745484	0.417161	0.578235
9	N02	digital_ok	0.00%	0.00%	29.57%	0.00%	-0.319487	-1.875919	-0.801178	-0.369865	-0.364162	-0.562153	-0.316461	2.177941	0.753885	0.428709	0.586857
10	N02	digital_ok	0.00%	0.00%	29.57%	0.00%	1.262231	-1.068565	-0.773343	-0.360292	0.354087	0.847330	1.350865	1.986729	0.744226	0.418075	0.588228
15	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.115259	0.458217	-1.095291	-1.047103	-0.626712	-1.274499	-0.360358	-0.168133	0.806829	0.508409	0.610304
16	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.972361	-1.280085	-0.246997	-1.063764	0.419379	1.180353	2.708424	-0.091008	0.808447	0.513633	0.604635
17	N01	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.179704	0.351964	0.240859	0.204972	-0.384923	-0.807943	3.347086	0.640909	0.809545	0.518182	0.608474
18	N01	RF_maintenance	100.00%	0.00%	100.00%	0.00%	8.108660	13.218214	2.559381	1.257628	1.172385	3.640456	11.594903	30.339676	0.798010	0.310124	0.643715
19	N02	digital_ok	100.00%	0.00%	16.13%	0.00%	-1.934205	-1.345005	1.274581	0.287055	1.052994	1.451497	7.412606	10.623693	0.748071	0.435948	0.571331
20	N02	digital_ok	0.00%	0.00%	29.57%	0.00%	-2.747258	2.018756	-0.560047	-0.230169	-0.963316	0.515650	0.181978	-0.937257	0.757092	0.423385	0.580005
21	N02	digital_ok	0.00%	0.00%	29.57%	0.00%	1.642751	-1.597406	0.376758	0.938820	0.323018	1.679581	1.995452	3.501548	0.748140	0.420998	0.580743
27	N01	RF_maintenance	100.00%	100.00%	100.00%	0.00%	31.983481	35.228902	25.165381	26.003809	13.836966	11.574374	3.489348	1.912456	0.036576	0.039901	0.002119
28	N01	RF_maintenance	100.00%	48.39%	100.00%	0.00%	22.294078	43.439170	-0.033139	1.136467	13.338306	12.908492	8.189309	27.155461	0.395754	0.154497	0.281713
29	N01	digital_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
30	N01	digital_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
31	N02	digital_ok	100.00%	0.00%	5.38%	0.00%	1.051284	-1.722033	-0.958989	-1.111017	-0.016086	5.618473	2.850470	2.567895	0.759870	0.444826	0.575815
32	N02	RF_maintenance	100.00%	0.00%	67.20%	0.00%	51.419364	44.286418	1.280876	0.784676	2.330268	1.320983	1.204436	-0.497536	0.649188	0.395007	0.389538
33	N02	RF_maintenance	100.00%	0.00%	100.00%	0.00%	0.572382	14.612434	1.668769	0.836484	-0.866316	2.077490	0.469892	25.721668	0.746267	0.280050	0.620802
36	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	16.254411	11.756293	-0.202944	-0.441096	0.145136	-0.042105	-0.208456	1.021058	0.808438	0.521630	0.583956
37	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	1.010508	1.032189	-0.866199	-1.050903	-1.125110	-0.917545	0.287852	8.918733	0.815699	0.534266	0.581619
38	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	0.245414	-1.000321	-1.074770	-1.048223	0.671174	1.725500	6.790422	2.467953	0.813709	0.537951	0.584810
40	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	0.461329	-0.467749	-0.979618	-0.816154	-0.445106	-0.480163	-0.386682	-0.858861	0.087962	0.095843	0.018954
41	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	0.139601	-0.417615	1.091992	0.387480	3.701201	-0.694506	-0.486483	-0.643479	0.051727	0.084203	0.013047
42	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	-0.647750	1.618223	0.616022	0.496619	-0.649778	-0.917434	0.017720	-0.005353	0.093965	0.095577	0.022445
45	N05	digital_ok	100.00%	0.00%	0.00%	0.00%	0.026876	0.413645	-1.092850	0.429003	0.883085	2.109787	-0.134458	17.670046	0.811865	0.498168	0.608881
46	N05	digital_ok	0.00%	0.00%	0.00%	0.00%	-1.703101	-0.893510	0.230529	-0.874099	0.782583	-0.738320	0.409430	1.586476	0.812993	0.492307	0.617082
50	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	20.920683	4.925897	-0.300929	1.380104	1.433052	-0.587517	0.934782	-0.930892	0.765450	0.534765	0.516152
51	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	2.065912	3.811513	-0.751726	0.252051	-0.228549	2.350991	0.111361	0.979445	0.811166	0.554277	0.562372
52	N03	RF_maintenance	100.00%	0.00%	0.00%	0.00%	13.189805	10.735482	0.194753	-0.298396	3.616832	-0.860846	1.382069	0.149262	0.817173	0.553165	0.565227
53	N03	digital_ok	100.00%	0.00%	0.00%	0.00%	2.980860	2.573658	-0.952767	0.060579	-1.348331	-0.710867	2.439495	6.805370	0.811792	0.558321	0.570786
54	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	1.596226	11.433258	0.992896	-0.579345	0.533210	9.220807	0.789468	1.014506	0.092629	0.092461	0.018723
55	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	3.118907	2.261657	0.539637	0.112830	9.350214	-0.994020	6.729819	-0.559940	0.070147	0.068133	0.007676
56	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	-1.060033	1.318273	0.862013	0.921620	-0.811100	2.890574	-0.252047	1.063588	0.068768	0.065877	0.007225
57	N04	RF_maintenance	100.00%	100.00%	100.00%	0.00%	52.385012	-0.621665	7.804106	1.726837	9.472987	-0.256581	6.409423	0.898648	0.115788	0.084275	0.018697
65	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	2.451882	0.440759	1.640359	0.481415	1.902845	0.431243	0.434365	-0.246724	0.812721	0.539330	0.578841
66	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	1.881605	1.119455	0.093297	0.313249	1.736729	0.427030	-0.450379	0.372458	0.814285	0.559865	0.556316
67	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	-1.007806	-1.372969	-0.236320	-1.090767	1.470121	-0.612093	0.632418	3.386544	0.814727	0.571846	0.545266
68	N03	digital_ok	0.00%	0.00%	0.00%	0.00%	1.755629	0.984665	1.895252	0.435833	1.917805	2.253015	0.005353	2.261437	0.809140	0.569962	0.546554
69	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	0.806418	-1.454738	0.064950	-0.089935	0.845602	1.671166	-0.219568	2.432565	0.103171	0.095797	0.025574
70	N04	RF_maintenance	100.00%	100.00%	100.00%	0.00%	11.622349	-1.751379	1.982080	-0.870516	3.747527	3.292810	1.244831	4.685262	0.077915	0.078935	0.012339
71	N04	digital_ok	0.00%	100.00%	100.00%	0.00%	0.301393	-0.056304	-0.793682	-0.530420	-0.378915	-0.941096	-0.274729	-0.418640	0.086907	0.083367	0.014719
72	N04	digital_ok	100.00%	100.00%	100.00%	0.00%	5.257168	-0.680048	-0.821762	0.973286	0.145441	0.925396	2.774753	-0.971438	0.096997	0.083042	0.015088
73	N05	digital_ok	100.00%	100.00%	0.00%	0.00%	29.718369	-0.594454	24.653983	0.296471	13.593285	0.590908	1.313026	0.896144	0.034165	0.526967	0.330161
81	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.267862	0.505093	-0.775543	2.291415	-0.639932	-0.218021	-0.058522	-1.148724	0.803235	0.531808	0.567044
82	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	3.864158	0.167563	0.840519	0.162011	1.371869	-0.939349	-0.199279	-1.014853	0.811480	0.549174	0.566313
83	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	1.099146	1.273175	1.315937	3.051195	-0.651990	1.157724	-0.704653	-1.115100	0.814850	0.575936	0.546525
84	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	14.959624	16.437997	0.972498	-0.062260	-1.461714	3.593770	0.099741	2.463329	0.817607	0.580605	0.539754
85	N08	digital_ok	0.00%	0.00%	0.00%	0.00%	0.147073	-0.054670	-0.919114	-0.805396	0.080354	-1.439282	-0.445875	-1.108229	0.807447	0.578079	0.556480
86	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	2.612611	7.248257	1.522499	-0.539403	10.066117	-0.787232	0.657750	-0.101843	0.808579	0.544194	0.562556
87	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	17.427273	17.035288	3.338683	0.485075	35.611650	0.134942	34.676322	4.336840	0.751318	0.572420	0.502411
88	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	17.015783	14.247318	19.431386	17.094088	11.940258	6.816889	0.007785	-0.212486	0.777511	0.542959	0.572016
90	N09	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-0.135991	0.356198	1.492901	2.075153	-1.316095	-0.167997	0.233548	0.660798	0.806341	0.532126	0.592563
91	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	13.511403	15.826799	17.586067	17.978579	9.652962	8.773463	-0.195817	0.362504	0.795540	0.515027	0.596422
92	N10	RF_maintenance	100.00%	100.00%	100.00%	0.00%	64.812401	85.600541	1.783631	3.192192	15.400782	24.331988	2.411606	14.371447	0.315399	0.217868	0.150512
93	N10	digital_ok	100.00%	0.00%	0.00%	0.00%	-0.020084	-0.352777	1.853802	-0.268932	6.179042	-0.376405	4.603769	-0.924084	0.742566	0.444905	0.562816
94	N10	digital_ok	100.00%	0.00%	0.00%	0.00%	-0.858645	-2.482219	-1.112090	-0.579565	0.975822	2.711670	1.641482	8.940240	0.743283	0.433951	0.573096
98	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.288022	1.047350	0.550868	1.343001	0.456007	0.877177	0.128436	1.260716	0.797188	0.516040	0.579665
99	N07	digital_ok	100.00%	0.00%	0.00%	0.00%	11.044924	0.648783	1.988090	0.327126	0.043554	5.283445	2.557328	-0.705514	0.810177	0.545881	0.564288
100	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.342834	-0.634211	-0.530266	-0.152394	-0.027911	-0.433469	-0.099519	-0.361538	0.813112	0.558751	0.563056
101	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	15.097577	15.626012	2.646290	1.019152	-0.081025	-1.159258	6.617130	-0.379930	0.822795	0.578496	0.554735
102	N08	RF_maintenance	100.00%	0.00%	77.96%	0.00%	8.945616	9.742282	28.758989	30.997029	539.661111	800.459223	6596.469809	6469.589375	0.724036	0.385234	0.556838
103	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	6.556263	15.750194	-0.787001	-0.255394	1.773840	2.787740	-0.107355	-0.791253	0.817382	0.585781	0.552573
104	N08	RF_maintenance	100.00%	0.00%	0.00%	0.00%	12.809707	118.484794	-0.349476	9.425889	2.817691	0.822321	0.304405	-0.553024	0.821689	0.570970	0.581521
105	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	1.948954	5.587118	7.670534	11.106371	1.384351	2.571353	-0.625767	0.205783	0.819578	0.572998	0.569377
106	N09	digital_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
107	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	7.121948	4.861614	8.856584	5.482929	1.492650	2.750113	0.342181	4.301728	0.820189	0.554531	0.576705
108	N09	digital_ok	100.00%	0.00%	0.00%	0.00%	9.742275	6.560330	13.965514	0.478123	16.860856	-0.682707	2.617190	-0.365023	0.711645	0.547482	0.535704
109	N10	digital_ok	0.00%	0.00%	0.00%	0.00%	0.097921	0.409664	0.042781	0.399700	1.336038	-0.686722	2.682357	0.549105	0.747288	0.470664	0.555252
110	N10	RF_maintenance	100.00%	0.00%	37.63%	0.00%	59.290589	42.000749	1.609925	0.700233	2.576981	4.728266	1.768138	13.422639	0.653993	0.404891	0.392314
111	N10	digital_ok	0.00%	0.00%	0.00%	0.00%	0.124125	0.482918	-1.041207	0.335717	0.027911	1.371464	-0.125581	3.264897	0.748773	0.451461	0.561166
112	N10	digital_ok	0.00%	0.00%	0.00%	0.00%	-1.243505	-0.497608	-0.852050	-0.318049	-0.441135	0.621008	-0.337279	-1.009985	0.739116	0.439137	0.567907
116	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	0.355467	3.221820	-0.971918	-0.662414	1.917438	-1.051050	0.268132	-0.874283	0.805626	0.519374	0.586232
117	N07	digital_ok	0.00%	0.00%	0.00%	0.00%	1.053550	0.660086	3.441466	3.568242	1.142015	-0.812564	-0.934431	-1.358033	0.813803	0.542959	0.577307
118	N07	digital_ok	100.00%	0.00%	100.00%	0.00%	2.743131	39.420295	1.744922	22.356034	0.949944	12.477860	1.071661	-0.162268	0.818528	0.049613	0.609283
119	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	2.066795	0.782338	6.897222	0.025826	1.012278	0.386303	-1.142903	-0.959381	0.827239	0.560731	0.573710
120	N08	RF_maintenance	100.00%	5.38%	100.00%	0.00%	26.538381	53.136457	0.098717	29.480536	10.153191	12.006507	0.988668	5.820736	0.450014	0.044562	0.351346
121	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	4.437166	8.467776	-0.903078	2.201983	-0.768391	1.730059	33.628889	15.058066	0.822809	0.585748	0.550847
122	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	13.868050	10.670390	0.892009	0.263279	1.095277	-0.162652	-0.422933	-0.936179	0.827368	0.582640	0.560969
123	N08	digital_ok	100.00%	0.00%	0.00%	0.00%	12.322598	15.018062	0.487739	1.597312	-0.642380	1.046899	0.778949	-0.368405	0.820230	0.582609	0.558241
125	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
126	N09	RF_maintenance	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
127	N10	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.577828	-0.261439	0.019299	-0.431933	2.452208	0.521991	-0.031978	0.890566	0.746765	0.480919	0.553014
128	N10	digital_ok	100.00%	0.00%	0.00%	0.00%	-1.852873	4.637942	-0.846238	0.095282	0.196134	2.705335	0.442398	-0.589073	0.748801	0.473084	0.552529
129	N10	digital_ok	0.00%	0.00%	0.00%	0.00%	-1.173483	-1.762398	0.446441	0.374382	-0.286178	0.239944	-0.688183	-0.917983	0.748684	0.463226	0.557143
130	N10	digital_ok	0.00%	0.00%	0.00%	0.00%	0.139973	0.011103	-0.354455	0.884986	0.637927	1.314835	0.422310	3.081987	0.737704	0.442619	0.559971
135	N12	digital_maintenance	0.00%	0.00%	0.00%	0.00%	-1.274468	-0.166524	-1.091725	-0.631776	0.161877	-0.126737	0.689732	-0.279811	0.739523	0.458771	0.547051
136	N12	digital_maintenance	100.00%	0.00%	0.00%	0.00%	2.151939	16.872246	-1.007059	-0.315165	1.460985	0.892365	1.867770	1.748974	0.739020	0.444756	0.528632
137	N07	RF_maintenance	100.00%	0.00%	0.00%	0.00%	6.393076	0.466526	10.500774	4.843067	5.829039	4.505292	-0.334483	0.059918	0.816053	0.544931	0.579541
138	N07	RF_maintenance	0.00%	0.00%	0.00%	0.00%	0.055082	-0.757047	1.881272	0.333327	-1.586033	0.294947	1.709772	-1.031858	0.813867	0.548539	0.577366
140	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	28.540762	33.669358	24.503432	26.026191	13.719541	11.561816	0.907147	0.660228	0.038178	0.039318	0.000443
141	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	2.649230	5.773857	2.155520	6.165710	-0.113652	0.214608	0.955313	17.447285	0.816067	0.542794	0.570379
142	N13	digital_ok	100.00%	0.00%	100.00%	0.00%	37.453153	41.349913	0.514366	26.218012	14.706527	11.594013	4.059624	1.908979	0.459285	0.039887	0.292931
143	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.208412	-1.230586	0.892922	-0.403340	0.533688	-0.662260	-0.081521	-1.217264	0.812439	0.575142	0.555145
144	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	-2.215628	-1.333906	1.171136	0.125909	-0.950184	-0.855247	0.484875	10.801160	0.820675	0.567878	0.571965
145	N14	RF_maintenance	100.00%	100.00%	100.00%	0.00%	32.719707	36.051376	25.373934	26.502639	14.000491	11.681720	2.629450	3.708079	0.033781	0.033432	-0.000142
150	N15	RF_maintenance	100.00%	100.00%	100.00%	0.00%	34.817844	38.454380	25.334217	27.089679	14.094390	11.804120	3.258747	3.356019	0.046472	0.046228	0.000573
155	N12	digital_maintenance	100.00%	100.00%	100.00%	0.00%	30.102733	31.610276	24.548538	25.748616	13.830607	13.647028	3.139416	4.463874	0.040164	0.039338	0.001507
156	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	0.849687	0.351395	0.435954	0.001946	-0.701442	-0.381714	3.274651	10.051129	0.753195	0.454241	0.552324
157	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.830340	-0.439740	-0.765571	1.817392	1.618646	0.274732	0.844800	0.764062	0.745827	0.472224	0.542945
158	N12	digital_ok	100.00%	100.00%	0.00%	0.00%	30.312127	-2.592428	25.241887	-1.088267	14.115732	-0.590575	1.146264	1.502837	0.036374	0.482653	0.295992
160	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
161	N13	digital_ok	100.00%	0.00%	0.00%	0.00%	-0.969164	61.806844	-0.249408	1.793948	-0.130133	1.157305	0.119545	-0.866670	0.810361	0.451025	0.554838
162	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	0.311813	0.197591	-1.009184	-0.991860	1.852178	0.471339	0.068956	-0.523801	0.816066	0.562026	0.575822
163	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.523124	-0.688895	-0.551055	-0.868038	-1.557011	0.632228	-0.334033	2.154181	0.812864	0.563588	0.567543
164	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	-1.965279	-1.508570	-1.159467	-0.504167	-1.083442	-0.162307	0.393753	1.598992	0.813578	0.556953	0.576457
165	N14	digital_ok	100.00%	0.00%	0.00%	0.00%	0.034609	2.167148	5.249283	-1.005023	0.528250	-1.067042	0.836580	-0.392201	0.814463	0.555273	0.574794
166	N14	RF_maintenance	100.00%	0.00%	0.00%	0.00%	27.910167	0.814955	0.214942	0.869797	9.912954	5.166901	18.406995	18.636143	0.734538	0.537687	0.503573
167	N15	digital_ok	100.00%	0.00%	2.69%	0.00%	29.137724	28.373581	15.377111	16.271672	10.387213	8.893505	1.540194	6.576261	0.691157	0.420606	0.462984
168	N15	RF_maintenance	100.00%	0.00%	0.00%	0.00%	11.738787	16.646965	16.074557	18.136689	9.220975	9.140040	-1.430201	-0.098289	0.800956	0.505222	0.592019
169	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	14.895902	15.310444	18.004373	17.092881	10.535737	9.023629	-0.517313	-0.442255	0.797605	0.492885	0.603754
170	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	15.240879	12.434884	18.178340	16.187023	10.937354	7.110400	-0.289237	-0.271709	0.792533	0.497992	0.604528
176	N12	digital_ok	0.00%	0.00%	2.69%	0.00%	0.811136	-0.847920	-1.069357	-0.342776	-0.614230	0.394954	-0.515027	-0.061096	0.749315	0.432096	0.572857
177	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	0.559973	0.251500	0.594607	2.116011	-0.108121	1.010696	0.015713	5.146140	0.742564	0.439689	0.556327
178	N12	digital_ok	0.00%	0.00%	0.00%	0.00%	-1.074487	-1.693622	0.042502	-0.798017	-0.216500	1.565360	0.073033	-0.790417	0.739534	0.461998	0.550184
179	N12	digital_ok	100.00%	0.00%	0.00%	0.00%	-0.195758	0.133129	-1.055984	-0.127496	3.396897	-1.190882	7.094316	-0.550181	0.740874	0.469646	0.554967
180	N13	RF_maintenance	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
181	N13	digital_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
182	N13	RF_maintenance	100.00%	0.00%	0.00%	0.00%	11.108815	8.303147	16.172813	14.614133	8.018944	0.176445	-1.288315	21.452844	0.804350	0.470877	0.604518
183	N13	digital_ok	0.00%	0.00%	0.00%	0.00%	-2.821035	-1.728960	-0.073893	-1.100906	-0.660366	0.952570	-0.419867	2.805222	0.809129	0.543580	0.585303
184	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.004625	-0.512906	-0.110757	0.142352	-0.911517	-0.628563	0.793736	-0.719936	0.806418	0.543183	0.577254
185	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	1.753599	0.054326	0.686567	0.256512	-0.499348	-1.352254	3.518121	-0.938957	0.811402	0.539310	0.585749
186	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	0.186267	-0.449840	2.670697	1.415567	2.902089	-0.328982	3.322219	-0.505167	0.801743	0.528852	0.585780
187	N14	digital_ok	0.00%	0.00%	0.00%	0.00%	-0.252834	0.029844	-0.375923	-0.019299	-0.554038	-0.630408	3.264376	1.574769	0.803506	0.534915	0.583805
189	N15	digital_ok	0.00%	0.00%	0.00%	0.00%	3.753033	3.531793	-0.206215	0.631335	-0.175739	3.124388	0.474914	2.781092	0.800060	0.503929	0.607761
190	N15	digital_ok	100.00%	0.00%	100.00%	0.00%	84.540198	39.737760	3.150667	26.496157	8.663956	11.783088	21.056259	2.743346	0.622478	0.043129	0.494284
191	N15	digital_ok	100.00%	0.00%	0.00%	0.00%	-1.138353	0.762635	-1.039365	1.415916	0.422031	-0.467818	1.021648	6.108255	0.808249	0.492490	0.630559
203	N18	RF_maintenance	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
220	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
221	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
222	N18	RF_ok	100.00%	100.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan
320	N03	dish_maintenance	100.00%	100.00%	100.00%	0.00%	40.521455	37.819743	16.563334	17.022129	13.558124	11.479568	6.056591	3.028863	0.052643	0.048172	0.003969
321	N02	not_connected	100.00%	0.00%	94.09%	0.00%	4.126250	2.283303	10.056158	8.859412	3.112359	2.169286	2.780778	2.436118	0.712977	0.328308	0.601060
323	N02	not_connected	100.00%	0.00%	96.77%	0.00%	37.953678	6.302466	0.683275	12.230396	4.653159	2.646106	4.850030	-0.916865	0.529778	0.312410	0.427062
324	N04	not_connected	100.00%	100.00%	100.00%	0.00%	9.181772	4.862696	13.755897	7.963718	3.862643	1.076854	0.147386	-0.768979	0.114940	0.083565	0.062163
329	N12	dish_maintenance	100.00%	0.00%	86.02%	0.00%	13.136093	1.790951	1.190784	8.122777	1.763219	0.654915	1.912595	-1.304542	0.643888	0.342368	0.533193
333	N12	dish_maintenance	100.00%	0.00%	94.09%	0.00%	11.461243	2.978659	0.852198	7.615543	1.993560	1.006801	3.343714	-0.500974	0.634236	0.331015	0.530139

# print ex_ants for easy copy-pasting to YAML file
proposed_ex_ants = [ant for i, ant in enumerate(ants) if np.any([col[i] > 0 for col in bar_cols.values()])]
print('ex_ants: [' + ", ".join(str(ant) for ant in proposed_ex_ants) + ']')
print(f'\nunflagged_ants: [{", ".join([str(ant) for ant in ants if ant not in proposed_ex_ants])}]')
# "golden" means no flags and good a priori status
golden_ants = ", ".join([str(ant) for ant in ants if ((ant not in proposed_ex_ants) and (a_priori_statuses[ant] in good_statuses.split(',')))])
print(f'\ngolden_ants: [{golden_ants}]')

ex_ants: [3, 4, 7, 8, 9, 10, 18, 19, 20, 21, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 40, 41, 42, 45, 50, 52, 53, 54, 55, 56, 57, 69, 70, 71, 72, 73, 84, 86, 87, 88, 91, 92, 93, 94, 99, 101, 102, 103, 104, 105, 106, 107, 108, 110, 118, 119, 120, 121, 122, 123, 125, 126, 128, 136, 137, 140, 141, 142, 144, 145, 150, 155, 156, 158, 160, 161, 165, 166, 167, 168, 169, 170, 176, 177, 179, 180, 181, 182, 190, 191, 203, 220, 221, 222, 320, 321, 323, 324, 329, 333]

unflagged_ants: [5, 15, 16, 17, 46, 51, 65, 66, 67, 68, 81, 82, 83, 85, 90, 98, 100, 109, 111, 112, 116, 117, 127, 129, 130, 135, 138, 143, 157, 162, 163, 164, 178, 183, 184, 185, 186, 187, 189]

golden_ants: [5, 15, 16, 17, 46, 51, 65, 66, 67, 68, 81, 83, 85, 98, 100, 109, 111, 112, 116, 117, 127, 129, 130, 143, 157, 162, 163, 164, 178, 183, 184, 185, 186, 187, 189]

# write to csv
outpath = os.path.join(nb_outdir, f'rtp_summary_table_{JD}.csv')
print(f'Now saving Table 2 to a csv at {outpath}')
df.to_csv(outpath)

Now saving Table 2 to a csv at /home/obs/src/H6C_Notebooks/_rtp_summary_/rtp_summary_table_2459832.csv

# Load antenna positions
data_list = sorted(glob.glob(os.path.join(data_path, f'zen.{JD}.?????.sum.uvh5')))
hd = io.HERAData(data_list[len(data_list) // 2])

# Figure out where to draw the nodes
node_centers = {}
for node in sorted(set(list(nodes.values()))):
    if np.isfinite(node):
        this_node_ants = [ant for ant in ants + unused_ants if nodes[ant] == node]
        if len(this_node_ants) == 1:
            # put the node label just to the west of the lone antenna 
            node_centers[node] = hd.antpos[ant][node] + np.array([-14.6 / 2, 0, 0])
        else:
            # put the node label between the two antennas closest to the node center
            node_centers[node] = np.mean([hd.antpos[ant] for ant in this_node_ants], axis=0)
            closest_two_pos = sorted([hd.antpos[ant] for ant in this_node_ants], 
                                     key=lambda pos: np.linalg.norm(pos - node_centers[node]))[0:2]
            node_centers[node] = np.mean(closest_two_pos, axis=0)

def Plot_Array(ants, unused_ants, outriggers):
    plt.figure(figsize=(16,16))
    
    plt.scatter(np.array([hd.antpos[ant][0] for ant in hd.data_ants if ant in ants]), 
                np.array([hd.antpos[ant][1] for ant in hd.data_ants if ant in ants]), c='w', s=0)

    # connect every antenna to their node
    for ant in ants:
        if nodes[ant] in node_centers:
            plt.plot([hd.antpos[ant][0], node_centers[nodes[ant]][0]], 
                     [hd.antpos[ant][1], node_centers[nodes[ant]][1]], 'k', zorder=0)

    rc_color = '#0000ff'
    antm_color = '#ffa500'
    autom_color = '#ff1493'

    # Plot 
    unflagged_ants = []
    for i, ant in enumerate(ants):
        ant_has_flag = False
        # plot large blue annuli for redcal flags
        if use_redcal:
            if redcal_flagged_frac[ant] > 0:
                ant_has_flag = True
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=7 * (2 - 1 * float(not outriggers)), fill=True, lw=0,
                                                color=rc_color, alpha=redcal_flagged_frac[ant]))
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=6 * (2 - 1 * float(not outriggers)), fill=True, color='w'))
        
        # plot medium green annuli for ant_metrics flags
        if use_ant_metrics: 
            if ant_metrics_xants_frac_by_ant[ant] > 0:
                ant_has_flag = True
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=6 * (2 - 1 * float(not outriggers)), fill=True, lw=0,
                                                color=antm_color, alpha=ant_metrics_xants_frac_by_ant[ant]))
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=5 * (2 - 1 * float(not outriggers)), fill=True, color='w'))
        
        # plot small red annuli for auto_metrics
        if use_auto_metrics:
            if ant in auto_ex_ants:
                ant_has_flag = True                
                plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=5 * (2 - 1 * float(not outriggers)), fill=True, lw=0, color=autom_color)) 
        
        # plot black/white circles with black outlines for antennas
        plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=4 * (2 - 1 * float(not outriggers)), fill=True, color=['w', 'k'][ant_has_flag], ec='k'))
        if not ant_has_flag:
            unflagged_ants.append(ant)

        # label antennas, using apriori statuses if available
        try:
            bgc = matplotlib.colors.to_rgb(status_colors[a_priori_statuses[ant]])
            c = 'black' if (bgc[0]*0.299 + bgc[1]*0.587 + bgc[2]*0.114) > 186 / 256 else 'white'
        except:
            c = 'k'
            bgc='white'
        plt.text(hd.antpos[ant][0], hd.antpos[ant][1], str(ant), va='center', ha='center', color=c, backgroundcolor=bgc)

    # label nodes
    for node in sorted(set(list(nodes.values()))):
        if not np.isnan(node) and not np.all(np.isnan(node_centers[node])):
            plt.text(node_centers[node][0], node_centers[node][1], str(node), va='center', ha='center', bbox={'color': 'w', 'ec': 'k'})
    
    # build legend 
    legend_objs = []
    legend_labels = []
    
    # use circles for annuli 
    legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgecolor='k', markerfacecolor='w', markersize=13))
    legend_labels.append(f'{len(unflagged_ants)} / {len(ants)} Total {["Core", "Outrigger"][outriggers]} Antennas Never Flagged')
    legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markerfacecolor='k', markersize=15))
    legend_labels.append(f'{len(ants) - len(unflagged_ants)} Antennas {["Core", "Outrigger"][outriggers]} Flagged for Any Reason')

    if use_auto_metrics:
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgewidth=2, markeredgecolor=autom_color, markersize=15))
        legend_labels.append(f'{len([ant for ant in auto_ex_ants if ant in ants])} {["Core", "Outrigger"][outriggers]} Antennas Flagged by Auto Metrics')
    if use_ant_metrics: 
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgewidth=2, markeredgecolor=antm_color, markersize=15))
        legend_labels.append(f'{np.round(np.sum([frac for ant, frac in ant_metrics_xants_frac_by_ant.items() if ant in ants]), 2)} Antenna-Nights on' 
                             f'\n{np.sum([frac > 0 for ant, frac in ant_metrics_xants_frac_by_ant.items() if ant in ants])} {["Core", "Outrigger"][outriggers]} Antennas '
                             'Flagged by Ant Metrics\n(alpha indicates fraction of time)')        
    if use_redcal:
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markeredgewidth=2, markeredgecolor=rc_color, markersize=15))
        legend_labels.append(f'{np.round(np.sum(list(redcal_flagged_frac.values())), 2)} Antenna-Nights on' 
                             f'\n{np.sum([frac > 0 for ant, frac in redcal_flagged_frac.items() if ant in ants])} {["Core", "Outrigger"][outriggers]} Antennas '
                             'Flagged by Redcal\n(alpha indicates fraction of time)')

    # use rectangular patches for a priori statuses that appear in the array
    for aps in sorted(list(set(list(a_priori_statuses.values())))):
        if aps != 'Not Found':
            legend_objs.append(plt.Circle((0, 0), radius=7, fill=True, color=status_colors[aps]))
            legend_labels.append(f'A Priori Status:\n{aps} ({[status for ant, status in a_priori_statuses.items() if ant in ants].count(aps)} {["Core", "Outrigger"][outriggers]} Antennas)')

    # label nodes as a white box with black outline
    if len(node_centers) > 0:
        legend_objs.append(matplotlib.patches.Patch(facecolor='w', edgecolor='k'))
        legend_labels.append('Node Number')

    if len(unused_ants) > 0:
        legend_objs.append(matplotlib.lines.Line2D([0], [0], marker='o', color='w', markerfacecolor='grey', markersize=15, alpha=.2))
        legend_labels.append(f'Anntenna Not In Data')
        
    
    plt.legend(legend_objs, legend_labels, ncol=2, fontsize='large', framealpha=1)
    
    if outriggers:
        pass
    else:
        plt.xlim([-200, 150])
        plt.ylim([-150, 150])        
       
    # set axis equal and label everything
    plt.axis('equal')
    plt.tight_layout()
    plt.title(f'Summary of {["Core", "Outrigger"][outriggers]} Antenna Statuses and Metrics on {JD}', size=20)    
    plt.xlabel("Antenna East-West Position (meters)", size=12)
    plt.ylabel("Antenna North-South Position (meters)", size=12)
    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)
    xlim = plt.gca().get_xlim()
    ylim = plt.gca().get_ylim()    
        
    # plot unused antennas
    plt.autoscale(False)    
    for ant in unused_ants:
        if nodes[ant] in node_centers:
            plt.plot([hd.antpos[ant][0], node_centers[nodes[ant]][0]], 
                     [hd.antpos[ant][1], node_centers[nodes[ant]][1]], 'k', alpha=.2, zorder=0)
        
        plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=4, fill=True, color='w', ec=None, alpha=1, zorder=0))
        plt.gca().add_artist(plt.Circle(tuple(hd.antpos[ant][0:2]), radius=4, fill=True, color='grey', ec=None, alpha=.2, zorder=0))
        if hd.antpos[ant][0] < xlim[1] and hd.antpos[ant][0] > xlim[0]:
            if hd.antpos[ant][1] < ylim[1] and hd.antpos[ant][1] > ylim[0]:
                plt.text(hd.antpos[ant][0], hd.antpos[ant][1], str(ant), va='center', ha='center', color='k', alpha=.2) 

Figure 1: Array Plot of Flags and A Priori Statuses

This plot shows all antennas, which nodes they are connected to, and their a priori statuses (as the highlight text of their antenna numbers). It may also show (depending on what is finished running):

• Whether they were flagged by auto_metrics (red circle) for bandpass shape, overall power, temporal variability, or temporal discontinuities. This is done in a binary fashion for the whole night.
• Whether they were flagged by ant_metrics (green circle) as either dead (on either polarization) or crossed, with the transparency indicating the fraction of the night (i.e. number of files) that were flagged.
• Whether they were flagged by redcal (blue circle) for high chi^2, with the transparency indicating the fraction of the night (i.e. number of files) that were flagged.

Note that the last fraction does not include antennas that were flagged before going into redcal due to their a priori status, for example.

core_ants = [ant for ant in ants if ant < 320]
outrigger_ants = [ant for ant in ants if ant >= 320]
Plot_Array(ants=core_ants, unused_ants=unused_ants, outriggers=False)
if len(outrigger_ants) > 0:
    Plot_Array(ants=outrigger_ants, unused_ants=sorted(set(unused_ants + core_ants)), outriggers=True)

Metadata

from hera_qm import __version__
print(__version__)
from hera_cal import __version__
print(__version__)

2.0.3.dev47+ga570afb
3.1.4.dev14+g122e1cb